Displaying publications 1 - 20 of 21 in total

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  1. Damanhuri NS, Chiew YS, Othman NA, Docherty PD, Pretty CG, Shaw GM, et al.
    Comput Methods Programs Biomed, 2016 Jul;130:175-85.
    PMID: 27208532 DOI: 10.1016/j.cmpb.2016.03.025
    BACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care.

    METHODS: Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort.

    RESULTS: The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort.

    CONCLUSION: A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care.

    Matched MeSH terms: Respiratory Mechanics*
  2. Ang CYS, Chiew YS, Vu LH, Cove ME
    Comput Methods Programs Biomed, 2022 Mar;215:106601.
    PMID: 34973606 DOI: 10.1016/j.cmpb.2021.106601
    BACKGROUND: Spontaneous breathing (SB) effort during mechanical ventilation (MV) is an important metric of respiratory drive. However, SB effort varies due to a variety of factors, including evolving pathology and sedation levels. Therefore, assessment of SB efforts needs to be continuous and non-invasive. This is important to prevent both over- and under-assistance with MV. In this study, a machine learning model, Convolutional Autoencoder (CAE) is developed to quantify the magnitude of SB effort using only bedside MV airway pressure and flow waveform.

    METHOD: The CAE model was trained using 12,170,655 simulated SB flow and normal flow data (NB). The paired SB and NB flow data were simulated using a Gaussian Effort Model (GEM) with 5 basis functions. When the CAE model is given a SB flow input, it is capable of predicting a corresponding NB flow for the SB flow input. The magnitude of SB effort (SBEMag) is then quantified as the difference between the SB and NB flows. The CAE model was used to evaluate the SBEMag of 9 pressure control/ support datasets. Results were validated using a mean squared error (MSE) fitting between clinical and training SB flows.

    RESULTS: The CAE model was able to produce NB flows from the clinical SB flows with the median SBEMag of the 9 datasets being 25.39% [IQR: 21.87-25.57%]. The absolute error in SBEMag using MSE validation yields a median of 4.77% [IQR: 3.77-8.56%] amongst the cohort. This shows the ability of the GEM to capture the intrinsic details present in SB flow waveforms. Analysis also shows both intra-patient and inter-patient variability in SBEMag.

    CONCLUSION: A Convolutional Autoencoder model was developed with simulated SB and NB flow data and is capable of quantifying the magnitude of patient spontaneous breathing effort. This provides potential application for real-time monitoring of patient respiratory drive for better management of patient-ventilator interaction.

    Matched MeSH terms: Respiratory Mechanics*
  3. Chiew YS, Tan CP, Chase JG, Chiew YW, Desaive T, Ralib AM, et al.
    Comput Methods Programs Biomed, 2018 Apr;157:217-224.
    PMID: 29477430 DOI: 10.1016/j.cmpb.2018.02.007
    BACKGROUND AND OBJECTIVE: Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation.

    METHODS: An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn).

    RESULTS AND DISCUSSION: Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%.

    CONCLUSION: The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.

    Matched MeSH terms: Respiratory Mechanics*
  4. Duncan MT, Husain R, Raman A, Cheah SH, Ch'ng SL
    Singapore Med J, 1990 Dec;31(6):543-7.
    PMID: 2281349
    Pulmonary function parameters were examined in a Malay Muslim population during normal activity and Ramadan fasting conditions. The validity of employing various lung function prediction formulae for the subjects was also assessed. Present findings indicate that the water deprivation regime and resultant dehydration during Ramadan did not cause significant changes in ventilatory functions. Although pulmonary prediction formulae based on Caucasian and African populations were inapplicable to the subjects examined, the equations derived from the neighbouring populations in Singapore could be employed.
    Matched MeSH terms: Respiratory Mechanics*
  5. Ang CYS, Chiew YS, Wang X, Mat Nor MB, Cove ME, Chase JG
    Comput Biol Med, 2022 Dec;151(Pt A):106275.
    PMID: 36375413 DOI: 10.1016/j.compbiomed.2022.106275
    BACKGROUND AND OBJECTIVE: Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment.

    METHODS: Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves.

    RESULTS: Clinical validation shows all three models captured more than 98% (median) of future Ers data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 

    Matched MeSH terms: Respiratory Mechanics
  6. Mohan SM, Reddy SC, Wei LY
    Int Ophthalmol, 2001;24(6):305-11.
    PMID: 14750567
    PURPOSE: To determine the effects of unilateral right/left nostril breathing (URNB/ULNB) and forced unilateral right/left nostril breathing (FURNB/FULNB) on intraocular pressure (IOP) and to examine the differences in the IOP during the various phases of nasal cycle.

    METHODS: Young healthy volunteers of either sex aged between 19-24 years, participated in the sessions using URNB/ULNB (n = 52) and FURNB/FULNB (n = 28). The nostril dominance was calculated from signals recorded on the PowerLab equipment, representing pressure changes at the end of the nostrils during respiration. The IOP was measured with Tono-Pen. The subjects were divided into 4 groups viz. right nostril dominant (RND), left nostril dominant (LND), transitional right nostril dominant (TRND) and transitional left nostril dominant (TLND) groups. The IOP data 'before and after' URNB/ULNB or FURNB/FULNB were compared by using paired t-test. The baseline data of IOP between the groups were analysed by using independent samples t-test.

    RESULTS: The URNB decreased the IOP in the LND and TLND (p < 0.01) and also in the RND (p < 0.05) groups but not significantly in the TRND group. The ULNB decreased the IOP in the RND group (p < 0.01) only. The FURNB significantly reduced the IOP (p < 0.05) only in the LND and RND groups. The FULNB decreased the IOP but not significantly. The baseline IOP did not differ significantly between the LND, RND, TLND and TRND groups.

    CONCLUSION: The URNB/FURNB reduced the IOP, while ULNB/FULNB failed to increase the IOP significantly. It is suggested that the lowering of IOP by URNB indicated sympathetic stimulation.

    Matched MeSH terms: Respiratory Mechanics/physiology*
  7. Zainol NM, Damanhuri NS, Othman NA, Chiew YS, Nor MBM, Muhammad Z, et al.
    Comput Methods Programs Biomed, 2022 Jun;220:106835.
    PMID: 35512627 DOI: 10.1016/j.cmpb.2022.106835
    BACKGROUND AND OBJECTIVE: Mechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can cause further ventilator-induced lung injury, increasing length of MV, cost and mortality. Patient-specific respiratory mechanics can help optimize MV settings. However, model-based estimation of respiratory mechanics is less accurate when patient exhibit un-modeled spontaneous breathing (SB) efforts on top of ventilator support. This study aims to estimate and quantify SB efforts by reconstructing the unaltered passive mechanics airway pressure using NARX model.

    METHODS: Non-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM Hospital in Kuantan, Malaysia. In this study, there are 2 different ratios of training and validating methods. Firstly, the initial ratio used is 60:40 which indicates 600 breath cycles for training and remaining 400 breath cycles used for testing. Then, the ratio is varied using 70:30 ratio for training and testing data.

    RESULTS AND DISCUSSION: The mean residual error between original airway pressure and reconstructed airway pressure is denoted as the magnitude of effort. The median and interquartile range of mean residual error for both ratio are 0.0557 [0.0230 - 0.0874] and 0.0534 [0.0219 - 0.0870] respectively for all patients. The results also show that Patient 2 has the highest percentage of SB incidence and Patient 10 with the lowest percentage of SB incidence which proved that NARX model is able to perform for both higher incidence of SB effort or when there is a lack of SB effort.

    CONCLUSION: This model is able to produce the SB incidence rate based on 10% threshold. Hence, the proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has the potential to further assist clinical decisions and optimize MV settings.

    Matched MeSH terms: Respiratory Mechanics
  8. Adli Azam MR, Raja Amin RM
    Malays J Med Sci, 2015 Jan-Feb;22(1):70-3.
    PMID: 25892952 MyJurnal
    Malignant chest wall tumour is rare. The presentation is usually aggressive that requires extensive resection to prevent recurrence. However, the extensive resection is to the expense of causing defect on the chest wall and hence, respiratory mechanics. Two cases of chest wall tumour are discussed including the surgical approach of radical tumour resection which was combined with placement of titanium mesh and Tranverse Rectus Abdominis Myocutaneus (TRAM) flap to cover the defect and preserve respiratory mechanical functions. The morbidity of using titanium mesh demonstrated in the case series were infection and injury to surrounding tissue due to its rigidity and large size which required its removal. However the formation of 'pseudopleura' made the thoracic cage return back as closed cavity even after the removal of the titanium mesh and allow normal respiratory functions.
    Matched MeSH terms: Respiratory Mechanics
  9. Zubair M, Abdullah MZ, Ahmad KA
    Comput Math Methods Med, 2013;2013:727362.
    PMID: 23983811 DOI: 10.1155/2013/727362
    The accuracy of the numerical result is closely related to mesh density as well as its distribution. Mesh plays a very significant role in the outcome of numerical simulation. Many nasal airflow studies have employed unstructured mesh and more recently hybrid mesh scheme has been utilized considering the complexity of anatomical architecture. The objective of this study is to compare the results of hybrid mesh with unstructured mesh and study its effect on the flow parameters inside the nasal cavity. A three-dimensional nasal cavity model is reconstructed based on computed tomographic images of a healthy Malaysian adult nose. Navier-Stokes equation for steady airflow is solved numerically to examine inspiratory nasal flow. The pressure drop obtained using the unstructured computational grid is about 22.6 Pa for a flow rate of 20 L/min, whereas the hybrid mesh resulted in 17.8 Pa for the same flow rate. The maximum velocity obtained at the nasal valve using unstructured grid is 4.18 m/s and that with hybrid mesh is around 4.76 m/s. Hybrid mesh reported lower grid convergence index (GCI) than the unstructured mesh. Significant differences between unstructured mesh and hybrid mesh are determined highlighting the usefulness of hybrid mesh for nasal airflow studies.
    Matched MeSH terms: Respiratory Mechanics/physiology
  10. Botelho DJ, Leo BF, Massa CB, Sarkar S, Tetley TD, Chung KF, et al.
    Nanotoxicology, 2016;10(1):118-27.
    PMID: 26152688 DOI: 10.3109/17435390.2015.1038330
    Multiple studies have examined the direct cellular toxicity of silver nanoparticles (AgNPs). However, the lung is a complex biological system with multiple cell types and a lipid-rich surface fluid; therefore, organ level responses may not depend on direct cellular toxicity. We hypothesized that interaction with the lung lining is a critical determinant of organ level responses. Here, we have examined the effects of low dose intratracheal instillation of AgNPs (0.05 μg/g body weight) 20 and 110 nm diameter in size, and functionalized with citrate or polyvinylpyrrolidone. Both size and functionalization were significant factors in particle aggregation and lipid interaction in vitro. One day post-intratracheal instillation lung function was assessed, and bronchoalveolar lavage (BAL) and lung tissue collected. There were no signs of overt inflammation. There was no change in surfactant protein-B content in the BAL but there was loss of surfactant protein-D with polyvinylpyrrolidone (PVP)-stabilized particles. Mechanical impedance data demonstrated a significant increase in pulmonary elastance as compared to control, greatest with 110 nm PVP-stabilized particles. Seven days post-instillation of PVP-stabilized particles increased BAL cell counts, and reduced lung function was observed. These changes resolved by 21 days. Hence, AgNP-mediated alterations in the lung lining and mechanical function resolve by 21 days. Larger particles and PVP stabilization produce the largest disruptions. These studies demonstrate that low dose AgNPs elicit deficits in both mechanical and innate immune defense function, suggesting that organ level toxicity should be considered.
    Matched MeSH terms: Respiratory Mechanics/drug effects*
  11. Redmond DP, Chiew YS, Major V, Chase JG
    Comput Methods Programs Biomed, 2019 Apr;171:67-79.
    PMID: 27697371 DOI: 10.1016/j.cmpb.2016.09.011
    Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.
    Matched MeSH terms: Respiratory Mechanics/physiology*
  12. Jahan I, Begum M, Akhter S, Islam MZ, Jahan N, Samad N, et al.
    Ann Afr Med, 2021 7 3;20(2):69-77.
    PMID: 34213471 DOI: 10.4103/aam.aam_114_20
    Introduction: Alternate nostril breathing (ANB) is an effective breathing exercise with therapeutic benefits on cardiorespiratory functions for healthy and diseased individuals. This study was conducted to assess the effects of ANB exercise on cardiorespiratory tasks in healthy adults.

    Materials and Methods: This randomized experimental study was conducted in the Department of Physiology, Chittagong Medical College, Chattogram, from July 2017 to June 2018. A total of 100 1st-year students, aged between 18 and 20 years, were included by a random sampling method. Fifty participants (25 males and 25 females) were enrolled in the experimental group, while age- and body mass index-matched another 50 participants (25 males and 25 females) served as the control group. Experimental group participants performed ANB exercise for 4 weeks. Cardiorespiratory parameters (pulse rate, blood pressure, forced vital capacity, forced expiratory volume in 1st s [FEV1], and peak expiratory flow rate [PEFR] were measured. Data were taken at the start and after 4 weeks in both groups.

    Results: Independent t-test showed no significant differences in the cardiorespiratory functions between the experimental and control groups among the male and female participants, except for the females' PEFR which showed small differences. On the other hand, repeated measure ANOVA shows significant improvement in the experimental groups among males (P < 0.001-0.028) and females (P < 0.001-0.001) in all the cardiorespiratory functions measured, except for the FEV1 and PEFR among males.

    Conclusion: The results of this study suggest that cardiorespiratory functions were improved after breathing exercise, and therefore, ANB can be recommended for increasing cardiorespiratory efficiency.

    Matched MeSH terms: Respiratory Mechanics/physiology*
  13. Arunachalam GR, Chiew YS, Tan CP, Ralib AM, Nor MBM
    Comput Methods Programs Biomed, 2020 Jan;183:105103.
    PMID: 31606559 DOI: 10.1016/j.cmpb.2019.105103
    BACKGROUND AND OBJECTIVE: Mechanical ventilation therapy of respiratory failure patients can be guided by monitoring patient-specific respiratory mechanics. However, the patient's spontaneous breathing effort during controlled ventilation changes airway pressure waveform and thus affects the model-based identification of patient-specific respiratory mechanics parameters. This study develops a model to estimate respiratory mechanics in the presence of patient effort.

    METHODS: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort.

    RESULTS AND DISCUSSION: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model was less than 10% for both simulated data and clinical trial patient data.

    CONCLUSION: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression.

    Matched MeSH terms: Respiratory Mechanics*
  14. Mohan V, Paungmali A, Sitilerpisan P, Hashim UF, Mazlan MB, Nasuha TN
    Nurs Health Sci, 2018 Jun;20(2):224-230.
    PMID: 29421851 DOI: 10.1111/nhs.12406
    Non-specific low back pain (NS-LBP) is known to cause respiratory dysfunction. In this study, we investigated alterations in breathing, respiratory strength and endurance, core stability, diaphragm mobility, and chest expansion among patients with NS-LBP and healthy individuals. The specific aim of the study was to correlate between respiratory function and other variables among NS-LBP patients. Thirty four patients with NS-LBP were matched with 34 healthy participants before undergoing total faulty breathing scale, spirometer, respiratory pressure meter, chest expansion, ultrasound, and pressure biofeedback measurements. There were signs of faulty breathing in the NS-LBP patients when compared to the healthy participants. Diaphragmatic mobility and respiratory muscle endurance were lower in the NS-LBP group. Chest expansion exhibited a significant decrease at the level of the fourth intercostal space in the NS-LBP group, but respiratory muscle strength and core stability were not significant between the two groups. Positive correlations were found to be fairly significant regarding respiratory muscle strength. The findings of this study indicated altered respiratory characteristics in the NS-LBP patients, and suggested that they would improve through respiratory exercises.
    Matched MeSH terms: Respiratory Mechanics/physiology*
  15. Lee JWW, Chiew YS, Wang X, Tan CP, Mat Nor MB, Damanhuri NS, et al.
    Ann Biomed Eng, 2021 Dec;49(12):3280-3295.
    PMID: 34435276 DOI: 10.1007/s10439-021-02854-4
    While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.
    Matched MeSH terms: Respiratory Mechanics/physiology*
  16. Tan AS, Wang CY
    Anaesth Intensive Care, 2010 Jan;38(1):65-9.
    PMID: 20191779
    The aim of this randomised, controlled trial was to determine the optimum dose of fentanyl in combination with propofol 2.5 mg x kg(-1) when inserting the Classic Laryngeal Mask Airway. Seventy-five ASA I or II patients were randomly assigned to five groups of fentanyl dosage: 0 microg x kg(-1) (placebo), 0.5 microg x kg(-1), 1.0 microg x kg(-1), 1.5 microg x kg(-1) and 2.0 microg x kg(-1). Anaesthesia was induced by first injecting the study drug over 10 seconds. Three minutes after the study drug was injected, propofol (2.5 mg x kg(-1)) was injected over 10 seconds. The Classic Laryngeal Mask Airway was inserted four minutes and 30 seconds after injection of the study drug. Insertion conditions were evaluated using a four-category score. Thirty-nine males and 36 females aged 19 to 59 years were studied. The incidence of prolonged apnoea increased as fentanyl dose increased. We found that there was a high rate of successful first attempt at insertion with 1 microg x kg(-1) and 1.5 microg x kg(-1), 93% and 87% respectively, compared to 87% in the 2.0 microg x kg(-1) group. The 1.0 microg x kg(-1) group also achieved an 80% optimal insertion conditions score of 4, compared to 73% in the 1.5 microg x kg(-1) group and 80% in the 2 microg x kg(-1) group. Therefore we recommend 1.0 microg x kg(-1) as the optimal dose of fentanyl when used in addition to propofol 2.5 mg/kg for the insertion of the Classic Laryngeal Mask Airway.
    Matched MeSH terms: Respiratory Mechanics/physiology
  17. Akyüz E, Üner AK, Köklü B, Arulsamy A, Shaikh MF
    J Neurosci Res, 2021 09;99(9):2059-2073.
    PMID: 34109651 DOI: 10.1002/jnr.24861
    Epilepsy is a debilitating disorder of uncontrollable recurrent seizures that occurs as a result of imbalances in the brain excitatory and inhibitory neuronal signals, that could stem from a range of functional and structural neuronal impairments. Globally, nearly 70 million people are negatively impacted by epilepsy and its comorbidities. One such comorbidity is the effect epilepsy has on the autonomic nervous system (ANS), which plays a role in the control of blood circulation, respiration and gastrointestinal function. These epilepsy-induced impairments in the circulatory and respiratory systems may contribute toward sudden unexpected death in epilepsy (SUDEP). Although, various hypotheses have been proposed regarding the role of epilepsy on ANS, the linking pathological mechanism still remains unclear. Channelopathies and seizure-induced damages in ANS-control brain structures were some of the causal/pathological candidates of cardiorespiratory comorbidities in epilepsy patients, especially in those who were drug resistant. However, emerging preclinical research suggest that neurotransmitter/receptor dysfunction and synaptic changes in the ANS may also contribute to the epilepsy-related autonomic disorders. Thus, pathological mechanisms of cardiorespiratory dysfunction should be elucidated by considering the modifications in anatomy and physiology of the autonomic system caused by seizures. In this regard, we present a comprehensive review of the current literature, both clinical and preclinical animal studies, on the cardiorespiratory findings in epilepsy and elucidate the possible pathological mechanisms of these findings, in hopes to prevent SUDEP especially in patients who are drug resistant.
    Matched MeSH terms: Respiratory Mechanics/physiology*
  18. Botelho D, Leo BF, Massa C, Sarkar S, Tetley T, Chung KF, et al.
    Front Pharmacol, 2018;9:213.
    PMID: 29632485 DOI: 10.3389/fphar.2018.00213
    Here we examine the organ level toxicology of both carbon black (CB) and silver nanoparticles (AgNP). We aim to determine metal-specific effects to respiratory function, inflammation and potential interactions with lung lining fluid (LLF). C57Bl6/J male mice were intratracheally instilled with saline (control), low (0.05 μg/g) or high (0.5 μg/g) doses of either AgNP or CB 15 nm nanospheres. Lung histology, cytology, surfactant composition and function, inflammatory gene expression, and pulmonary function were measured at 1, 3, and 7 days post-exposure. Acutely, high dose CB resulted in an inflammatory response, increased neutrophilia and cytokine production, without alteration in surfactant composition or respiratory mechanics. Low dose CB had no effect. Neither low nor high dose AgNPs resulted in an acute inflammatory response, but there was an increase in work of breathing. Three days post-exposure with CB, a persistent neutrophilia was noted. High dose AgNP resulted in an elevated number of macrophages and invasion of lymphocytes. Additionally, AgNP treated mice displayed increased expression of IL1B, IL6, CCL2, and IL10. However, there were no significant changes in respiratory mechanics. At day 7, inflammation had resolved in AgNP-treated mice, but tissue stiffness and resistance were significantly decreased, which was accompanied by an increase in surfactant protein D (SP-D) content. These data demonstrate that the presence of metal alters the response of the lung to nanoparticle exposure. AgNP-surfactant interactions may alter respiratory function and result in a delayed immune response, potentially due to modified airway epithelial cell function.
    Matched MeSH terms: Respiratory Mechanics
  19. Rogers BD, Rengarajan A, Abrahao L, Bhatia S, Bor S, Carlson DA, et al.
    Neurogastroenterol Motil, 2021 06;33(6):e14009.
    PMID: 33094875 DOI: 10.1111/nmo.14009
    BACKGROUND: Esophagogastric junction contractile integral (EGJ-CI) and EGJ morphology are high-resolution manometry (HRM) metrics that assess EGJ barrier function. Normative data standardized across world regions and HRM manufacturers are limited.

    METHODS: Our aim was to determine normative EGJ metrics in a large international cohort of healthy volunteers undergoing HRM (Medtronic, Laborie, and Diversatek software) acquired from 16 countries in four world regions. EGJ-CI was calculated by the same two investigators using a distal contractile integral-like measurement across the EGJ for three respiratory cycles and corrected for respiration (mm Hg cm), using manufacturer-specific software tools. EGJ morphology was designated according to Chicago Classification v3.0. Median EGJ-CI values were calculated across age, genders, HRM systems, and regions.

    RESULTS: Of 484 studies (28.0 years, 56.2% F, 60.7% Medtronic studies, 26.0% Laborie, and 13.2% Diversatek), EGJ morphology was type 1 in 97.1%. Median EGJ-CI was similar between Medtronic (37.0 mm Hg cm, IQR 23.6-53.7 mm Hg cm) and Diversatek (34.9 mm Hg cm, IQR 22.1-56.1 mm Hg cm, P = 0.87), but was significantly higher using Laborie equipment (56.5 mm Hg cm, IQR 35.0-75.3 mm Hg cm, P 

    Matched MeSH terms: Respiratory Mechanics
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